NVIDIA/garak

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https://github.com/NVIDIA/garak · scanned 2026-05-15 16:00 UTC (3 weeks ago) · 10 languages

169 findings (18 legacy + 151 scanner) 81st percentile · Python · medium (20-100K LoC) Scanner says 76 (higher by 3)

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Complete repo analysis

Last scanned 3 weeks ago · v1 · 13 findings from 1 source. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

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Score breakdown â 2026-05-17-v4 calibration-aware

Component	Sub-score	Weight	Contribution
`structure_score`	60.0	0.15	9.00
`security_score`	83.5	0.25	20.88
`testing_score`	100.0	0.20	20.00
`documentation_score`	81.0	0.15	12.15
`practices_score`	65.0	0.15	9.75
`code_quality`	73.1	0.10	7.31
Overall		1.00	79.1

Calibrated penalty buckets (security_score): threat: 16.5

Severity distribution — click a segment to filter

Active filters: layer: software × excluding tests × Reset all

Severity: Critical 0 High 8 Medium 4 Low 0 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 13 9-layer 0 Crowd 0 Layer: Security 6 Quality 6 Software 1

Exclude dismissed / FP Exclude test files

Scan summary Repository scanned at 76.3/100 with 100.0% coverage. It contains 2975 nodes across 3 cross-layer flows, written primarily in mixed languages. Engine surfaced 0 findings. Risk profile is low: 0 critical, 0 high, 0 medium. Recommended next step: open the software layer findings first — that's where the highest-impact wins live.

Showing 1 of 13 findings. Click TP / FP to vote on a finding's accuracy — votes adjust the confidence weighting and improve detection across the platform.

high Legacy software dependency [BINARY] scipy: compound risk score 2194 (CVEs: 0, binary findings: 550)

Review binary security profile of scipy — consider alternatives with lower binary attack surface

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